Student Researcher - Multiple Teams

About the team The Seed LLM Post Training team is responsible for researching cutting-edge posttrain technologies and providing core posttrain capabilities for unified multimodal large models. The team's goal is to research and explore next-generation advanced technologies such as SFT, RM, RL, and self-learning during the posttrain phase, while significantly optimizing and improving key areas including reasoning, coding, agent, and omni model. We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date). Responsibilities - Explore large-scale models and optimize systems. - Data construction, instruction tuning, preference alignment, and model optimization. - Improving relevant model capabilities, such as reasoning, code, math etc. - In-depth research and exploration of future use cases. Minimum Qualifications: - Currently pursuing a PhD in Computer Science, AI, or a related field. - Research experience in reinforcement learning, sequential decision-making, or agent behavior. - First-author publications in accredited ML/AI conferences (e.g., NeurIPS, ICLR, ICML). - Solid programming and experimentation skills, including with RL or LLM frameworks. - Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Preferred Qualifications: - Experience with LLM agents, tool use, or prompt-based control. - Familiarity with environments such as WebArena, ALFWorld, or programmatic reasoning tasks. - Understanding of RL techniques such as reward shaping, memory augmentation, or curriculum learning. As a condition of employment, all successful candidates must be able to establish authorization to work in the United States. For this position, the Company does not provide sponsorship or any immigration-related benefits.